By using an optimised semi-quantitative method, we studied the composition of microbial communities using PCR-DGGE of 16S rRNA genes , we identified the predominant species in the bacterial community, and investigated the species abundance in continuously fed MFCs. The dominant species in the stable community are α-, β-, γ-, δ- and ε-Proteobacteria and members of the phyla Firmicutes, Bacteroidetes, Actinobacteria and Spirochaetes. These species present electrogenic, fermentative and non-fermentative metabolism.
The model was built combining metabolic models available for the species present in the community and contains all the metabolic reactions in the community, including those involved in electron transfer to the anode, and can be used to identify the metabolic limitations affecting MFC performance. Using the model, it is possible to simulate processes using microbial consortia, varying their composition or the nature or concentration of the feeding substrate and using linear programming approaches (Flux Balance Analysis and Flux Variability Analysis) to optimise the distribution of metabolic fluxes towards the maximisation of the electric output. The findings of the in-silico approach have been compared to experimental data obtained in replicate MFCs.